The present disclosure relates to a methodology for evaluating a network for robustness employing a graph and a system for implementing the same.
The ability to monitor, model, and simulate flow parameters on a network is vital in planning, constructing, and maintaining the network in a condition that is robust enough to withstand disruptive events. The ability to monitor, model and simulate traffic conditions on roads of large cities and metropolitan areas has become increasingly essential for transportation agencies and other organizations involved in providing services to citizens (such as the police, fire departments or time-sensitive delivery companies) and planning more robust infrastructure in the path towards smarter cities. Due to the increasing frequency of adverse weather events, cities have to deal with emergency situations during which certain areas may experience difficult road conditions or be rendered completely inaccessible. Identifying the possible disconnections of the road network (meaning the unavailability of a given set of roads that results in a certain part of the city being isolated) and determining their impact on traffic conditions are important factors that city planners and infrastructure managers would like to consider when modeling and simulating traffic so that alternative mitigating measures can be planned in advance or investments be determined in order to avoid such situations.
Further, supply chain network of a distributor or an assembly line of a product that requires many components are vulnerable to disruptions. For example, throughput of a car assembly line or an airplane assembly line can be significantly decreased when certain components are not delivered in time for any reason, including geopolitical conflicts and natural disasters. In addition, electricity supply network can be disrupted by anticipated or unanticipated natural disasters.
Performing simulations on a large network, however, can be both time and resource consuming as the network often comprises a large mathematical graph (“graph” hereafter) with more than tens of thousands of nodes and edges. The currently known approaches for simulating disconnections in a network employ examination of combinations of edge disconnections. However, considering k-edge-disconnected cases for an n-edge graph, produces disconnection scenarios on an order of nk, i.e., n to the k-th power. As the number of n increases, the number of disconnections can easily become an astronomical number that even modern computers cannot handle.